
AT&T Database of Faces (AT&T) [1]} contains face images acquired in the laboratory conditions, with a dark homogeneous background. Subjects are captured in an upright, frontal position. There are 10 different images of each of 40 subjects. Covariates include lighting, facial expressions, open or closed eyes, as well as facial accessories such as glasses.

Labeled Faces in the Wild (LFW) [2]} was designed for studying the problem of unconstrained face recognition. The dataset contains 5,749 distinct subjects with 13,233 images of faces collected from the web and hence incorporates highly unconstrained conditions. We select 9,164 images of 1,680 subjects, with two or more images in the dataset. Faces were detected and cropped by the Viola-Jones face detector [3]}.

IMDb-Face (IMDb) [4]} is a large-scale noise-controlled dataset for face recognition research, containing about 1.7M faces of 59K identities, which is a manually cleaned subset of the original 2M raw images. All images were obtained from the IMDb website. We select 1,167,509 images associated to 10,347 identities. IMDb contains gender and age annotations, which allow us to perform additional experiments on subsets pertained to gender and age. See statistics in Table REF .

ND-TWINS-2009-2010 (TWINS) [5]} dataset was collected by the University of Notre Dame in the years 2009-2010 and comprises 24,050 color photographs of 435 attendee faces captured at the Twins Days Festival under natural light in indoor and outdoor configurations. Facial yaw varies from \(-90\)  to \(+90\)  degrees in steps of 45 degrees. We use a set of cropped 23,762 images, where we detect faces with the MTCNN detector [6]}.

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